bilingual-roman-urdu-embedder

dataset_size: 77 loss: CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 pipeline_tag: sentence-similarity library_name: sentence-transformers 🇵🇰 Bilingual Roman Urdu + English Sentence Embedder A lightweight Sentence Transformer model designed for Roman Urdu + English semantic understanding, optimized for:

🔍 Semantic Search 💬 Chatbots / FAQ Retrieval 🏠 Real Estate Query Matching 🌐 Cross-lingual Similarity (Roman Urdu ↔ English)

This model maps sentences into a 384-dimensional dense vector space.

🚀 Quick Usage
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("embedingHG/bilingual-roman-urdu-embedder")

# Roman Urdu
emb1 = model.encode(["yeh ghar kitne ka hai"])

# English
emb2 = model.encode(["what is the price"])

---

tags:

• sentence-transformers
• sentence-similarity
• feature-extraction
• generated_from_trainer
• dataset_size:77
• loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget:
• source_sentence: plot size please sentences:
  • land area
  • is this available
  • hello
• source_sentence: area size sentences:
  • it is cheap
  • location kahan hay
  • total square footage
• source_sentence: parking available sentences:
  • property price
  • location kia hay
  • hi
• source_sentence: available hay kia sentences:
  • kia yeh khali hay
  • how many bedrooms
  • thanks
• source_sentence: location kia hay sentences:
  • thanks a lot
  • how many square feet
  • yeh ghar kis area main hay pipeline_tag: sentence-similarity library_name: sentence-transformers

---

---

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True}) (2): Normalize({}) )

Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library:

pip install -U sentence-transformers
#Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model")
# Run inference
sentences = [
    'location kia hay',
    'yeh ghar kis area main hay',
    'how many square feet',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8117, 0.5783],
#         [0.8117, 1.0000, 0.7344],
#         [0.5783, 0.7344, 1.0000]])

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Training Details

Training Dataset

Unnamed Dataset

• Size: 77 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 77 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 3 tokens</li><li>mean: 6.03 tokens</li><li>max: 11 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 5.75 tokens</li><li>max: 12 tokens</li></ul>	<ul><li>min: 0.08</li><li>mean: 0.84</li><li>max: 0.95</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>kitne bedrooms hain</code>	<code>bedrooms ki tadaad kia hay</code>	<code>0.87</code>
  <code>what is the total area</code>	<code>how many square feet</code>	<code>0.92</code>
  <code>yeh property kahan hay</code>	<code>address batao</code>	<code>0.85</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss",
      "cos_score_transformation": "torch.nn.modules.linear.Identity"
  }
  

Training Hyperparameters Non-Default Hyperparameters per_device_train_batch_size: 16 num_train_epochs: 50 per_device_eval_batch_size: 16 multi_dataset_batch_sampler: round_robin All Hyperparameters Click to expand Training Time Training: 2.1 minutes Framework Versions Python: 3.12.3 Sentence Transformers: 5.4.1 Transformers: 5.5.4 PyTorch: 2.11.0+cpu Accelerate: 1.13.0 Datasets: 4.8.4 Tokenizers: 0.22.2 Citation BibTeX Sentence Transformers @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", }

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